People feel they understand complex phenomena with far greater precision, coherence, and depth than they really do; they are subject to an illusion—an illusion of explanatory depth. The illusion is far stronger for explanatory knowledge than many other kinds of knowledge, such as that for facts, procedures or narratives. The illusion for explanatory knowledge is most robust where the environment supports real‐time explanations with visible mechanisms. We demonstrate the illusion of depth with explanatory knowledge in Studies 1–6. Then we show (...) differences in overconfidence about knowledge across different knowledge domains in Studies 7–10. Finally, we explore the mechanisms behind the initial confidence and behind overconfidence in Studies 11 and 12, and discuss the implications of our findings for the roles of intuitive theories in concepts and cognition. (shrink)

The rise of appeals to intuitive theories in many areas of cognitive science must cope with a powerful fact. People understand the workings of the world around them in far less detail than they think. This illusion of knowledge depth has been uncovered in a series of recent studies and is caused by several distinctive properties of explanatory understanding not found in other forms of knowledge. Other experimental work has shown that people do have skeletal frameworks of expectations that constrain (...) richer ad hoc theory construction on the fly. These frameworks are supplemented by an ability to evaluate and rely on the division of cognitive labour in one's culture, an ability shown to be present even in young children. (shrink)

Choosing the right plan is often choosing the more coherent plan: but what is coherence? We argue that coherence-directed practical inference ought to be represented computationally. To that end, we advance a theory of deliberative coherence, and describe its implementation in a program modelled on Thagard's ECHO. We explain how the theory can be tested and extended, and consider its bearing on instrumentalist accounts of practical rationality.

This paper considers an application of work on probabilistic measures of coherence to inference to the best explanation. Rather than considering information reported from different sources, as is usually the case when discussing coherence measures, the approach adopted here is to use a coherence measure to rank competing explanations in terms of their coherence with a piece of evidence. By adopting such an approach IBE can be made more precise and so a major objection to this mode of reasoning can (...) be addressed. Advantages of the coherence - based approach are pointed out by comparing it with several other ways to characterize ‘ best explanation ’ and showing that it takes into account their insights while overcoming some of their problems. The consequences of adopting this approach for IBE are discussed in the context of recent discussions about the relationship between IBE and Bayesianism. (shrink)

Classical models of decision making deal fairly well with uncertainty, where settings are well-structured in terms of goals, alternatives, and consequences. Conversely, the typical ill-structured nature of strategy choices remains a challenge for extant models. Such cases can hardly build on the past, and their novelty makes the prediction of consequences a very difficult and poorly robust task. The weakness of the classical expected utility model in representing such problems has not been adequately solved by recent extensions. In this paper (...) we offer an explanatory coherence model for decision making in ill-structured problems. We model alternatives as sets of concurrent causal explanations of reality that act as justifications for action. According to these premises, choice is based on an evaluation of the internal coherence and the consistency of competing explanations of the available evidence. This model is psychologically grounded on causal inference and builds on the connectionist tradition of explanatory coherence. To illustrate the model, we consider the decision of investing in a new technology and we discuss how changes in the structure of alternatives may impact on the solution. We show how the final choice depends on collecting the relevant evidence, making the suitable hypotheses, and drawing the consistent causal explanations linking the two. (shrink)

This paper defines in a formal and computational way the notion of ‘consilience’, a term introduced by Whewell in 1847 for the evaluation of scientific theories. Informally, as has been used to date, a model or theory is ‘consilient’ if it is predictive, explanatory and unifies the evide-nce. Centred in a constructive framework, where new terms can be intro-duced, we essay a formalisation of the idea of unification based on the avoidance of ‘sepa-ration’. However, it is soon manifest that this (...) classical approach is vulnerable to the introduction of fantastic concepts to unify disparate sub-theories. Our second approach is constructed by using a detailed evaluation of the relationship between the theory and the evid-ence by means of reinforcement propagation. With the use of reinfor-cement, fantastic concepts can be better detected and the role of con-silience for theory construction and revision can be specialised for dif-ferent inference mechanisms like explanatory induction, abduction, deduc-tion and analogy. (shrink)

The relationship between machine learning and the philosophy of science can be classed as a dynamic interaction: a mutually beneficial connection between two autonomous fields that changes direction over time. I discuss the nature of this interaction and give a case study highlighting interactions between research on Bayesian networks in machine learning and research on causality and probability in the philosophy of science.